Dynamic difficulty adjustment (DDA) is a method of automatically modifying a game's
features, behaviors, and scenarios in real‐time, depending on the player's skill, so that the …

The focus of this survey is on research in applying evolutionary and other metaheuristic
search algorithms to automatically generating content for games, both digital and nondigital …

This paper describes the Mario AI benchmark, a game-based benchmark for reinforcement
learning algorithms and game AI techniques developed by the authors. The benchmark is …

We introduce a procedural content generation (PCG) framework at the intersections of
experience-driven PCG and PCG via reinforcement learning, named ED (PCG) RL, EDRL in …

Tanagra is a mixed-initiative tool for level design, allowing a human and a computer to work
together to produce a level for a 2-D platformer. An underlying, reactive level generator …

We have developed a modular virtual reality gaming application that can be used to
synthesize exercise drills for racket sports. By defining cost terms that are related to the …

Launchpad is an autonomous level generator that is based on a formal model of 2-D
platformer level design. Levels are built out of small segments called “rhythm groups,” which …

While many open-ended digital games feature non-linear storylines and multiple solution
paths, it is challenging for game developers to create effective game experiences in these …

" Player modeling" is a loose concept. It can equally apply to everything from a predictive
model of player actions resulting from machine learning to a designer's description of a …

Goal recognition is the task of inferring users' goals from sequences of observed actions. By
enabling player-adaptive digital games to dynamically adjust their behavior in concert with …